Meta-analysis Techniques in EpidemiologyCollate, analyze and conclude using results of several related studies.

Dr Sakshi Dubey, M.V.Sc. Scholar, Division of Epidemiology

&

Dr. BR Singh, Head Division of Epidemiology Indian Veterinary Research Institute, Izatnagar & Director CCS

NIAH, Baghpat

Analysis of analyses

Collate, analyze and conclude using results of several related studies

The statistical analysis of a collection of analytic results for the purpose of

integrating their findings (DerSimonian and Laird, 1986)

Published studies from literature are combined (Berman and Parker,

2002)

Weighted analysis of summary statistics (Bravata and Olkin,

2001)

Frequently used for clinical trials

Benefits of Meta-analysis

Offers more reliable information

Increases precision in estimating effects

Gain in statistical power of conclusions

Determines if new studies are needed to further investigate an issue

Meta-analysis

Meta-analysis ???

Single study Deliver reliable information for only specific place or period Diverse results due to varying places or periods or both Incapable in providing final conclusions

Interest How far results of individual studies are stable under varying situations Provide valid results for wider population

Combined statistical analysis is necessary To produce overall summary of result To determine consistency among different studies

Meta-analysis

Overcomes the limits of size or scope in single studies.

History The 12th century in China, Chu Hsi (1130~1200)

formulated 'Theory of Systematic Rule’

In Western World (17th century) studies of astronomy

In 1904 Karl Pearson in the British Medical Journal, published a paper on multiple clinical studies

In the 1970s, meta analysis was introduced in educational research, starting with the work of Gene V. Glass, Frank L. Schmidt and John E. Hunter.

Function of Meta-Analysis Identifies heterogeneity

Increases statistical power and precision of the study

Develop ,refine, and tests hypothesis

Calculates sample size for future studies

Identifies data gaps

Reduces the subjectivity of study comparisons

Advantages

Focuses attention on trials as an evaluation tool to increase the impact of trials on clinical practice.

Encourages designing of good trial and increases strength of conclusions.

Make the results fit for generalising to a larger population.

Improves precision and accuracy of estimates through use of more data sets.

May increase the statistical power to detect an effect.

Advantages Inconsistency of results across studies can be

quantified, analyzed and corrected.

Hypothesis testing can be applied on summary estimates.

Moderators can be included to explain variation between studies.

The presence of publication bias can be investigated.

Disadvantages Meta-analysis may discourage large definitive

trials. Increases tendency to unwittingly mix different

trials and ignore differences. Potential for tension between meta-analyst and

conductors of original trials may introduce biasness.

Meta-analysis of several small studies may not predict the results of a single large study.

Sources of bias are not controlled by the method A good meta-analysis of badly designed studies

will still result in bad statistics.

Steps of Meta-analysis Define the Research Question

Perform the literature search

Select the studies

Extract the data

Analyze the data

Report the results

Study Sources Published literature

citation indexes

abstract databases

reference lists

contact with authors

Unpublished literature

Uncompleted research reports

Work in progress

Quality Assessment

Study components Study design

Outcome measurement Exposure measurement

Response rate/follow-up rate

Analytic strategy Adjustment for confounding

Quality of reporting

Data Extraction Publication year

Performing year

Study design

Characteristics of study population (n, age, sex)

Geographical setting

Assessment procedures

Risk estimate and variance

Covariates

Funnel Plot “A funnel plot is used as a way to assess

publication bias in meta-analysis.”

Comparability of sources

Key feature of component trial is the

variability (heterogeneity)

Heterogeneity is variation between the

studies’ results

Statistical measures of heterogeneity The Chi2 test measures the amount of variation in

a set of trials, and tells us if it is more than would be expected by chance.

I squared quantifies heterogeneity.

where Q = heterogeneity c2 statistic

Higgins and Thompson (2002)

Q

dfQI

1002

Types of models are used to produce summary effect measures

1• Fixed Effect Model

2• Random Effects Model

3• Meta-Regression

Fixed effect model Inference is based on the studies actually

done.

The variance component of the summary effect is only composed of terms for the within study variance of each study.

Confidence intervals too narrow.

Random Effect Model Inference is based on the assumption that

studies used in the analysis are a random sample of a hypothetical population of studies.

Variance component includes a between study component as well as a within study component.

Confidence interval is wide or wider than in fixed effect model.

Models and Measures

Model Effect Assumption Methods

Measures

Fixed effect model Mantel – Haenszel approach

Ratios (Odds -ratios, rate ratios, risk ratio)

Peto method Odds ratio

General Variance Based

Ratio all types and rate difference

Random effect model

DerSimonian and Laird

Ratio (all types) and rate difference

^

Meta-regression is a tool used in meta-analysis to examine the impact of moderator variables on study effect size using regression-based techniques.

Meta-regression is a technique which allows researchers to explore the types of patient-specific factors or study design factors contributing to the heterogeneity.

Meta-Regression

Forest plot The graphical display of results from

individual studies on a common scale is a “Forest plot”

Useful tool for epidemiological studies which investigates the relationships between certain risk factors and disease. (Dutton, 2010)

Useful tool to improve animal well-being and productivity

Despite of a wealth of suitable studies it is relatively underutilized in animal and veterinary science. Lean et al. (2009)

Meta-analysis can provide reliable results about diseases occurrence, pattern and impact in livestock.

It is utmost essential to take benefit of this statistical tool for produce. more reliable estimates of concern effects in animal and veterinary science data.

Meta-analysis for animal and veterinary science

Conclusion Prior to conducting a meta-analysis, it is necessary to

determine if the purpose is to explore sources of heterogeneity or to calculate a summary effect size.

Each Steps of Meta-analysis is very important. Source of data should be free from publication biasness. Follows GIGO principle of ‘garbage in, garbage out’. Like large epidemiologic studies, meta-analysis run the risk of

appearing to give results more precise and conclusive that are warranted.